Abstract
This paper analyzes drivers of rising per-pupil public education spending, including Baumol’s “cost disease” effect. Empirical analyses using a large dataset of advanced and developing economies show that the contribution of Baumol’s effect was much smaller than implied by theory. Rather, the increase in per-pupil spending reflects rising wage premiums paid for teachers in excess of market wages, especially in developing countries. The strong wage premium effect suggests that institutional characteristics that govern teachers’ wage-setting are key determinants of education expenditure.
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Notes
Nordhaus (2008) provides evidence that the Baumol’s cost disease hypothesis holds in the USA based on the industry account data from the Bureau of Economic Analysis for the period 1948–2001.
For example, in Portugal, the recurrent cost of public education is large. About 95 % is spent on compensation for teaching and non-teaching staffs (IMF 2013).
The wage premium would be positive and increase over time for two reasons: (a) a higher skill premium for teachers and (b) higher rent payments, for example, because of strong collective bargaining power of labor union in the education sector. Positive union wage effect in education is found around the world in the literature Geeta and Teal (2010) for India; Lemke (2004) for the USA; and Liang (2000) for Latin American countries.
Additional empirical results as well as details about the specification of panel unit root and cointegration tests are available in the working paper version of this paper (Nose 2015).
The Baumol’s effect might be stronger if the sample could cover earlier years before 1995. However, the Baumol’s effect on PEE remains to be weak in any empirical models used in later subsections, which consistently provides little support for the Baumol’s hypothesis in education.
The estimates remain robust when per capita GDP growth and the change in teacher-pupil ratio are replaced with per-pupil GDP growth and the change in school-age population.
Glewwe and Kremer (2006) and UNESCO (2011) point out that there is scarcity of trained teachers at the primary level especially in SSA and South Asia, where the student-teacher ratio is quite high compared with other regions. While the access to basic education is still limited in low-income countries, Barro and Lee (2015) and Hanushek and Woessmann (2015) also emphasized that the quality of spending is more critical than the quantity to fill the gap in school attainment and achievement between developed and developing countries.
Psacharopoulos and Patrinos (2004) presents the latest estimates of the return to education covering 98 countries, which shows (a) falling returns to education as the economy develops and (b) increasing private returns to higher education. The returns are estimated to be the highest for developing countries, especially in Latin America and the SSA.
The sample omits many LICs as teachers’ wage data are not available. As the government needs to pay competitive salaries as other occupations to attract skilled workers in education sector especially in LICs, this sample selection is likely to attenuate the Baumol’s effect and the wage premium effect for low-income groups, and therefore, the estimates in Table 4 would only represent the lower bound of the true effect.
In contrast, the vector error correction estimate found that the long-term relationship between real GDP per capita and public education spending is stronger in non-advanced economies than advanced economies as consistent with the Wagner’s law. Akitoby et al. (2006) also found the similar supporting evidence that the Wagner’s law holds for general government spending in developing countries. The result is available upon request.
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Acknowledgments
The author is grateful to Andreas Haufler, two anonymous referees, Vitor Gaspar, Sanjeev Gupta, David Coady, Masahiro Nozaki, Baoping Shang, Benedict Clements, and seminar participants at the IMF’s Fiscal Affairs Department for insightful comments. The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management.
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Appendix: Data description
Appendix: Data description
The following 61 countries are included as the main sample in our econometric analysis.
The following series are used for the analysis in the main text.
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Nose, M. Estimation of drivers of public education expenditure: Baumol’s effect revisited. Int Tax Public Finance 24, 512–535 (2017). https://doi.org/10.1007/s10797-016-9410-7
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DOI: https://doi.org/10.1007/s10797-016-9410-7